Advanced architectures have been proposed for efficient orthorectification of digital airborne camera images,
including a system based on GPU processing and distributed computing able to geocorrect three digital still
aerial photographs per second. Here, we address the computationally harder problem of geocorrecting image
data from airborne pushbroom sensors, where each individual image line has associated its own camera attitude
and position parameters. Using OpenGL and CUDA interoperability and projective texture techniques, originally
developed for fast shadow rendering, image data is projected onto a Digital Terrain Model (DTM) as if by a slide
projector placed and rotated in accordance with GPS position and inertial navigation (IMU) data. Each line is
sequentially projected onto the DTM to generate an intermediate frame, consisting of a unique projected line
shaped by the DTM relief. The frames are then merged into a geometrically corrected georeferenced orthoimage.
To target hyperband systems, avoiding the high dimensional overhead, we deal with an orthoimage of pixel
placeholders pointing to the raw image data, which are then combined as needed for visualization or processing
tasks. We achieved faster than real-time performance in a hyperspectral pushbroom system working at a line rate
of 30 Hz with 200 bands and 1280 pixel wide swath over a 1 m grid DTM, reaching a minimum processing speed
of 356 lines per second (up to 511 lps), over eleven (up to seventeen) times the acquisition rate. Our method
also allows the correction of systematic GPS and/or IMU biases by means of 3D user interactive navigation.
We aim at the discrimination of varieties within a single plant species (<i>Vitis vinifera</i>) by means of airborne
hyperspectral imagery collected using a CASI-2 sensor and supervised classification, both under constant and
varying within-scene illumination conditions. Varying illumination due to atmospheric conditions (such as clouds)
and shadows cause different pixels belonging to the same class to present different spectral vectors, increasing
the within class variability and hindering classification. This is specially serious in precision applications such
as variety discrimination in precision agriculture, which depends on subtle spectral differences. In this study,
we use machine learning techniques for supervised classification, and we also analyze the variability within and
among plots and within and among sites, in order to address the generalizability of the results.